A Practical Tutorial on Explainable AI Techniques
- URL: http://arxiv.org/abs/2111.14260v1
- Date: Sat, 13 Nov 2021 17:47:31 GMT
- Title: A Practical Tutorial on Explainable AI Techniques
- Authors: Adrien Bennetot, Ivan Donadello, Ayoub El Qadi, Mauro Dragoni, Thomas
Frossard, Benedikt Wagner, Anna Saranti, Silvia Tulli, Maria Trocan, Raja
Chatila, Andreas Holzinger, Artur d'Avila Garcez, Natalia D\'iaz-Rodr\'iguez
- Abstract summary: This tutorial is meant to be the go-to handbook for any audience with a computer science background.
It aims at getting intuitive insights of machine learning models, accompanied with straight, fast, and intuitive explanations out of the box.
- Score: 5.671062637797752
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Last years have been characterized by an upsurge of opaque automatic decision
support systems, such as Deep Neural Networks (DNNs). Although they have great
generalization and prediction skills, their functioning does not allow
obtaining detailed explanations of their behaviour. As opaque machine learning
models are increasingly being employed to make important predictions in
critical environments, the danger is to create and use decisions that are not
justifiable or legitimate. Therefore, there is a general agreement on the
importance of endowing machine learning models with explainability. The reason
is that EXplainable Artificial Intelligence (XAI) techniques can serve to
verify and certify model outputs and enhance them with desirable notions such
as trustworthiness, accountability, transparency and fairness. This tutorial is
meant to be the go-to handbook for any audience with a computer science
background aiming at getting intuitive insights of machine learning models,
accompanied with straight, fast, and intuitive explanations out of the box. We
believe that these methods provide a valuable contribution for applying XAI
techniques in their particular day-to-day models, datasets and use-cases.
Figure \ref{fig:Flowchart} acts as a flowchart/map for the reader and should
help him to find the ideal method to use according to his type of data. The
reader will find a description of the proposed method as well as an example of
use and a Python notebook that he can easily modify as he pleases in order to
apply it to his own case of application.
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